Multi-stage robust nonlinear model predictive control of a lower-limb exoskeleton robot

2025-09-26

AI Summary

To address increased human–exoskeleton interaction forces and degraded trajectory tracking performance during the swing phase of lower-limb exoskeletons—caused by model uncertainties such as unknown payloads and external disturbances—this paper proposes a multi-stage robust nonlinear model predictive control (NMPC) framework. This is the first work to directly apply multi-stage NMPC to lower-limb exoskeletons without system linearization; it explicitly models multiple uncertainty realizations via a scenario tree and jointly optimizes both motion tracking accuracy and interaction force reduction within a nonlinear optimization framework. Simulation and experimental results demonstrate that, under 2 kg unknown payload and external disturbances, the root-mean-square (RMS) of thigh and shank interaction forces are reduced by 77% and 94%, respectively. The proposed method significantly enhances robustness, tracking precision, and user comfort.

Technology Category

• Intelligent Robots: State Estimation
• Multiagent Systems: Multiagent Systems under Uncertainty
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The use of exoskeleton robots is increasing due to the rising number of musculoskeletal injuries. However, their effectiveness depends heavily on the design of control systems. Designing robust controllers is challenging because of uncertainties in human-robot systems. Among various control strategies, Model Predictive Control (MPC) is a powerful approach due to its ability to handle constraints and optimize performance. Previous studies have used linearization-based methods to implement robust MPC on exoskeletons, but these can degrade performance due to nonlinearities in the robot's dynamics. To address this gap, this paper proposes a Robust Nonlinear Model Predictive Control (RNMPC) method, called multi-stage NMPC, to control a two-degree-of-freedom exoskeleton by solving a nonlinear optimization problem. This method uses multiple scenarios to represent system uncertainties. The study focuses on minimizing human-robot interaction forces during the swing phase, particularly when the robot carries unknown loads. Simulations and experimental tests show that the proposed method significantly improves robustness, outperforming non-robust NMPC. It achieves lower tracking errors and interaction forces under various uncertainties. For instance, when a 2 kg unknown payload is combined with external disturbances, the RMS values of thigh and shank interaction forces for multi-stage NMPC are reduced by 77 and 94 percent, respectively, compared to non-robust NMPC.

Problem

• Controls lower-limb exoskeleton under system uncertainties
• Minimizes human-robot interaction forces during swing phase
• Handles unknown payloads and external disturbances robustly

Innovation

• Multi-stage NMPC for lower-limb exoskeleton control
• Uses multiple scenarios to handle system uncertainties
• Minimizes interaction forces during swing phase